ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery

• URL: http://arxiv.org/abs/2605.12784v2
• Date: Thu, 14 May 2026 05:07:36 GMT
• Title: ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
• Abstract summary: $textttToolMol$ is an evolutionary agentic framework for de novo drug design.<n>$textttToolMol$ achieves state-of-the-art results in gold-standard Absolute Binding Free Energy scores.
• Score: 21.51115935236214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advances in large language models (LLMs) have recently opened new and promising avenues for small-molecule drug discovery. Yet existing LLM-based approaches for molecular generation often suffer from high rates of invalid and low-quality ligand candidates, a result of the syntactic limitations of current models with regard to molecular strings. In this paper, we introduce $\texttt{ToolMol}$, an evolutionary agentic framework for de novo drug design. $\texttt{ToolMol}$ combines a multi-objective genetic algorithm with an agentic LLM operator that iteratively updates the ligand population. We build a comprehensive toolbox of RDKit-backed functions that allows our agentic operator to consisently make precise ligand modifications. $\texttt{ToolMol}$ achieves state-of-the-art performance on multi-objective property optimization tasks, discovering drug-like and synthesizable ligands that have $>10\%$ stronger predicted binding affinity compared to existing methods, evaluated on three protein targets. $\texttt{ToolMol}$ ligands additionally achieve state-of-the-art results in gold-standard Absolute Binding Free Energy scores, gaining over existing methods by over $35\%$. By studying chain-of-thought reasoning traces, we observe that tool-calling enables the model to more faithfully execute its planned modifications, efficiently exploiting the strong chemical prior knowledge in LLMs.

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